Readability formulas: Useful or useless?

McClure, G. M.

doi:10.1109/tpc.1987.6449109

Cited by 55 publications

(22 citation statements)

References 7 publications

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“…Readability of the patient letters was partially determined using the Flesch-Kincaid grade score. The Flesch-Kincaid grade score has been used as a measure of readability since its development in 1975, is the standard used in Department of Defense manuals (McClure 1987) and is recommended by the Centers for Disease Control and Prevention as a way to determine readability (2009). The Flesch-Kincade grade calculates the reading level (US school grade level) at which the reader can comprehend at least 50 % of the document.…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Readability of Genetic Counseling Patient Letters

Brown

Skinner

Ashley

et al. 2015

Journal of Genetic Counseling

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Patient letters are a powerful tool that genetic counselors use to communicate with their patients. Patient letters are often sent to provide information on a new diagnosis, reiterate test results, and to serve as a permanent record of the visit. Patient letters, however, are only helpful if the patients can understand them. More than 50 % of the US population reads below a 9th grade reading level and over one-third of the population has low health literacy skills. In this study we evaluate the readability of genetic counseling patient letters by assessing reading level, image use, and terminology use. One hundred forty-nine genetic counselors participated in the survey and of these, 79 submitted a sample patient letter. Analyses of the letters revealed a mean reading level of 10.93. On average, 6 genetic terms were included in each letter, and only 25 % of these terms were defined. Analyses of survey responses revealed over 75 % of the genetic counselors did not include images in their patient letters. These results indicate there is room for improvement in order to make genetic counseling patient letters more accessible to the general population.

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Section: Discussionmentioning

confidence: 99%

Assessment of the Readability of Genetic Counseling Patient Letters

Brown

Skinner

Ashley

et al. 2015

Journal of Genetic Counseling

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“…Another common type of document quality approximations are content-based. These are numerous and diverse, including for instance, ratios of information-to-noise, of stopwords per document, or of document words per stopword list [4,46,47]; average term length per document [17]; term part-ofspeech [25,26]; ratio of technical terminology per (scientific) document [20]; ratio of non-compositional phrases per document [29]; syllable, term and/or sentence statistics [37] as per standard readability indices [8,15,19,27,28]; discourse structure [24]; document entropy computed from terms [4] or discourse entities [34]. The lexical or syntactic features used in the above content-based document quality approximations are assumed to indicate syntactic or semantic difficulty.…”

Section: Related Workmentioning

confidence: 99%

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

Lioma

Tarissan

Simonsen

et al. 2016

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

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Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a number of other text processing tasks, including Information Retrieval (IR), where adjusting the ranking of documents according to both their relevance and their coherence has been shown to increase retrieval effectiveness [34,37].The state of the art in unsupervised coherence modelling represents documents as bipartite graphs of sentences and discourse entities, and then projects these bipartite graphs into one-mode undirected graphs. However, one-mode projections may incur significant loss of the information present in the original bipartite structure. To address this we present three novel graph metrics that compute document coherence on the original bipartite graph of sentences and entities. Evaluation on standard settings shows that: (i) one of our coherence metrics beats the state of the art in terms of coherence accuracy; and (ii) all three of our coherence metrics improve retrieval effectiveness because, as closer analysis reveals, they capture aspects of document quality that go undetected by both keyword-based standard ranking and by spam filtering. This work contributes document coherence metrics that are theoretically principled, parameter-free, and useful to IR.

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“…The above approaches are developed stand-alone, not as an integral part of IR systems. However, methods for potentially integrating such text quality metrics into IR systems abound, for instance when text quality is interpreted as ratios of (combinations of) stopwords over content words per document [4,64,65]; term length [27] or part-of-speech (for ranking [38,41], but also for index pruning [38]); technical [33] or ambiguous scientific terminology [36]; non-compositional multiword expressions [39,47]; document readability [9,19,29,43,44]; document discourse [37] or coherence [52,40].…”

Section: Related Workmentioning

confidence: 99%

A study of factuality, objectivity and relevance

Lioma

Larsen

et al. 2016

Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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Much of the information processed by Information Retrieval (IR) systems is unreliable, biased, and generally untrustworthy [15,45,48]. Yet, factuality & objectivity detection is not a standard component of IR systems, even though it has been possible in Natural Language Processing (NLP) in the last decade. Motivated by this, we ask if and how factuality & objectivity detection may benefit IR. We answer this in two parts. First, we use state-of-the-art NLP to compute the probability of document factuality & objectivity in two TREC collections, and analyse its relation to document relevance. We find that factuality is strongly and positively correlated to document relevance, but objectivity is not. Second, we study the impact of factuality & objectivity to retrieval effectiveness by treating them as query independent features that we combine with a competitive language modelling baseline. Experiments with 450 TREC queries show that factuality improves precision by more than 10% over strong baselines, especially for the type of uncurated data typically used in web search; objectivity gives mixed results. An overall clear trend is that document factuality & objectivity is much more beneficial to IR when searching uncurated (e.g. web) documents vs. curated (e.g. state documentation and newswire articles).To our knowledge, this is the first study of factuality & objectivity for back-end IR, contributing novel findings about the relation between relevance and factuality/objectivity, and statistically significant gains to retrieval effectiveness in the competitive web search task.

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Readability formulas: Useful or useless?

Cited by 55 publications

References 7 publications

Assessment of the Readability of Genetic Counseling Patient Letters

Assessment of the Readability of Genetic Counseling Patient Letters

Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval

A study of factuality, objectivity and relevance

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